aixterm 0.1.1

Terminal AI assistant with MCP support

AIxTerm

An AI-powered command-line assistant that helps you discover and execute shell commands through natural language queries. AIxTerm integrates with Large Language Models to provide intelligent, context-aware assistance for your terminal operations.

Features

Core Functionality

• 🤖 Natural Language Interface: Ask questions in plain English and get executable shell commands
• Safe Command Execution: Built-in safety checks with user confirmation for potentially dangerous commands
• Context-Aware: Maintains session history and provides relevant context to improve responses
• Cross-Platform: Works on Windows, macOS, and Linux with platform-specific command detection

Enhanced Context System

• File Context Integration: Include specific files as context using --file flag (can be used multiple times)
• Smart Context Summarization: Intelligently summarizes terminal history to provide relevant context without overwhelming the LLM
• Project Detection: Automatically detects project types (Python, Node.js, Java, etc.) for better context
• Directory Analysis: Provides intelligent summaries of current directory structure and important files

Advanced Features

• MCP Server Support: Integrates with Model Context Protocol servers for extended functionality
• 🧹 Automatic Cleanup: Manages log files and temporary data with configurable cleanup policies
• Token Management: Intelligent token counting and content truncation for optimal LLM performance
• Command Extraction: Advanced regex patterns to extract commands from various code block formats

Developer-Friendly

• Comprehensive Testing: Full test coverage with 160+ tests including cross-platform compatibility
• Modular Architecture: Clean separation of concerns with dedicated modules for different functionality
• Configurable: Extensive configuration options via JSON config files
• Well Documented: Comprehensive documentation and type hints throughout

Installation

Prerequisites

• Python 3.8+
• pip or conda

Install from PyPI (Recommended)

pip install aixterm

Install from Source

git clone https://github.com/yourusername/aixterm.git
cd aixterm
pip install -e .

Development Installation

git clone https://github.com/yourusername/aixterm.git
cd aixterm
make venv  # Sets up virtual environment and installs dependencies
source venv/bin/activate

Configuration

AIxTerm uses a configuration file located at ~/.aixterm_config.json. You can create this file manually or let AIxTerm create it with default values on first run.

Example Configuration

{
  "api_url": "http://localhost/v1/chat/completions",
  "api_key": "",
  "model": "llama2",
  "streaming": true,
  "context_tokens": 500,
  "timeout": 30,
  "mcp_servers": [],
  "cleanup": {
    "enabled": true,
    "max_log_files": 50,
    "max_log_age_days": 30,
    "max_log_size_mb": 10,
    "cleanup_interval_hours": 24
  }
}

Configuration Options

• api_url: URL of your LLM API endpoint
• api_key: API key for authentication (if required)
• model: Model name to use
• streaming: Enable streaming responses
• context_tokens: Maximum tokens to include from terminal history
• timeout: Request timeout in seconds
• mcp_servers: List of MCP server configurations
• cleanup: Automatic cleanup settings

Usage

Basic Usage

# Ask a simple question
ai "how do I list all running processes?"

# Get help with specific commands
ai "what does the grep command do?"

# Ask for file operations
ai "how do I find large files in the current directory?"

Planning Mode

# Use planning mode for complex tasks
ai --plan "set up a complete CI/CD pipeline for my Python project"

# Planning mode with file context
ai -p --file docker-compose.yml "optimize this deployment for production"

# Complex system administration planning
ai --plan "create a disaster recovery strategy for our database"

File Context Integration

# Include single file as context
ai --file script.py "how can I improve this code?"

# Include multiple files
ai --file main.py --file config.py "analyze the architecture of this application"

# Combine with queries
ai --file requirements.txt "what Python packages does this project use and what are they for?"

Management Commands

# Show AIxTerm status
ai --status

# List available MCP tools
ai --tools

# Force cleanup now
ai --cleanup

# Show help
ai --help

Advanced Features

Smart Context System

AIxTerm includes an intelligent context system that:

1. Analyzes Current Directory: Automatically detects project types and important files
2. Summarizes Terminal History: Provides relevant recent commands and outputs
3. Manages Token Limits: Intelligently truncates content to stay within model limits
4. Prioritizes Recent Activity: Focuses on the most recent and relevant information

Terminal Session Logging

For optimal context, AIxTerm automatically logs terminal sessions to provide better context for AI responses. The logging system:

• Automatic Log Files: Creates session-specific logs at ~/.aixterm_log.{tty_name}
• TTY Detection: Automatically detects current terminal session using TTY information
• Conversation History: Maintains structured conversation records for context
• Smart Summarization: Intelligently summarizes terminal history to avoid overwhelming the LLM

Setting Up Shell Integration

For optimal context, AIxTerm can automatically capture all terminal activity. Use the built-in installation command:

Automatic Installation (Recommended)

# Install shell integration for automatic terminal logging
ai --install-shell

# For different shells
ai --install-shell --shell zsh
ai --install-shell --shell fish

# Uninstall if needed
ai --uninstall-shell

Manual Methods To capture all terminal activity manually, you can set up shell integration:

Method 1: Using script command

# Start a logged session
script -f ~/.aixterm_log.$(tty | sed 's/\/dev\///g' | sed 's/\//-/g')
# Then use aixterm normally in this session

Method 2: Add to ~/.bashrc manually

# Basic terminal logging for aixterm
log_for_aixterm() {
    local tty_name=$(tty 2>/dev/null | sed 's/\/dev\///g' | sed 's/\//-/g')
    local log_file="$HOME/.aixterm_log.${tty_name:-default}"
    echo "$ $BASH_COMMAND" >> "$log_file" 2>/dev/null
}
trap 'log_for_aixterm' DEBUG

MCP Server Integration

AIxTerm supports Model Context Protocol (MCP) servers for extended functionality:

{
  "mcp_servers": [
    {
      "name": "file-manager",
      "command": ["python", "file_manager_server.py"],
      "enabled": true
    }
  ]
}

Automatic Cleanup

The cleanup system automatically manages:

• Log file rotation and deletion
• Temporary file cleanup
• Configurable retention policies
• Background cleanup scheduling

Development

Project Structure

aixterm/
├── __init__.py
├── main.py           # Main application logic
├── config.py         # Configuration management
├── context.py        # Context and history management
├── llm.py           # LLM client integration
├── mcp_client.py    # MCP server integration
├── cleanup.py       # Cleanup management
└── utils.py         # Utility functions

tests/
├── test_*.py        # Comprehensive test suite
└── conftest.py      # Test configuration

aixterm.py            # CLI entry point
requirements.txt     # Dependencies
setup.py            # Package setup

Running Tests

# Run all tests
python -m pytest tests/

# Run with coverage
python -m pytest tests/ --cov=aixterm

# Run specific test file
python -m pytest tests/test_integration.py -v

Testing Results

• 160 tests passing
• 0 tests skipped
• Full cross-platform compatibility
• 82% code coverage with comprehensive integration tests

Examples

Code Analysis

# Analyze Python code
ai --file app.py "what does this application do and how can I improve it?"

# Review configuration
ai --file docker-compose.yml "explain this Docker setup"

# Security review
ai --file script.sh "are there any security issues in this script?"

System Administration

# Process management
ai "how do I kill a process by name?"

# Disk usage
ai "show me disk usage by directory"

# Network diagnostics
ai "how do I check if a port is open?"

Development Workflow

# Git operations
ai "how do I undo the last commit?"

# Package management
ai --file requirements.txt "update these Python packages to latest versions"

# Planning complex development tasks
ai --plan "refactor this monolithic application into microservices"

# Debugging with planning approach
ai -p "debug a memory leak in my Python application"

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add tests for new functionality
4. Ensure all tests pass (python -m pytest tests/)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with support for various LLM providers (OpenAI, Ollama, etc.)
• Integrates with Model Context Protocol for extensibility
• Inspired by the need for intelligent command-line assistance